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Information Technologies, Knowledge Ecology, and Firm Performance: An Exploratory Study

Information Technologies, Knowledge Ecology, and Firm Performance: An Exploratory Study. T.P. Liang D.N. Chen National Sun Yat-sen University Workshop on KE and EC, Aug 23, 2003. Knowledge Management. Research in knowledge management has covered various technical and managerial issues.

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Information Technologies, Knowledge Ecology, and Firm Performance: An Exploratory Study

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  1. Information Technologies, Knowledge Ecology, and Firm Performance: An Exploratory Study T.P. Liang D.N. Chen National Sun Yat-sen University Workshop on KE and EC, Aug 23, 2003

  2. Knowledge Management • Research in knowledge management has covered various technical and managerial issues. • Among the managerial research, the process view that stresses the process of knowledge creation, storage, retrieval, transfer, and applications, seems to dominate.

  3. A Strategic View • Another view is more from a strategic perspective that treats knowledge as a strategic investment that must be managed with cost effectiveness. • In other words, different industries may need to take different strategies and maintaining a different knowledge ecology.

  4. Research Problem • Should an industry focus on a few key categories of knowledge or a broad coverage of all knowledge in order to be competitive? • Does the adoption of IT have any relationship with the value of knowledge and firm performance?

  5. Ecological model in Organization • Hannon and Freeman (1989) proposed the ecological view of organization that seeks to understand how social conditions affect the rates in which new organizations and new organizational forms arises, the rates at which organizations change forms, and the rates at which organizations die out.

  6. A Knowledge Ecology • Basic species in a knowledge ecology is different types of knowledge that belong to the organization. • The goal of KM is to build a mechanism by which a healthy balance of knowledge can be maintained for achieving superior performance.

  7. Diversity vs. Stability • In ecological rules, the diversity-stability relationship is a major principal, which says an ecology is more stable if it maintains a certain level of diversity. • Similarly, we would like to examine whether the same rule holds in a knowledge ecology, ie, organizations with more diversified knowledge are more stable in performance.

  8. Research Framework

  9. Hypotheses (1) • H1: Relationship Between IT and Knowledge Ecology • H11: Higher IT capabilities support higher knowledge ecology • H12: Higher IT capabilities support higher knowledge diversity

  10. Hypotheses (2) • Relationship between Knowledge diversity and firm performance • H21: Higher knowledge intensity results in higher average performance • H22: Higher knowledge intensity results in lower performance variations

  11. Hypotheses (3) • Relationship between knowledge diversity and firm performance • Higher knowledge diversity results in lower average performance • Higher knowledge diversity results in lower performance variations

  12. Criteria for Choosing Industries • Four industries were chosen based on their knowledge intensity and environmental uncertainty. • Knowledge intensity is measured as the ratio of product price by the tangible costs (including material costs and depreciation of fixed assets). • Environmental uncertainty is measured by the changes in technology (measured by the number and importance of patents) and product lifecycle.

  13. The Chosen Industries

  14. Twelve Knowledge Types • Twenty companies were chosen (five in each category) for study. • Value chain activities are used to differentiate 12 categories of knowledge, such as raw material acquisition, product manufacturing, distribution, marketing, customer services, strategic planning, general management, financial management, quality management, human resource management, R&D, and IS management.

  15. Data Collection • A group of experts was invited to fill out the questionnaire for assessing the relative importance of a particular knowledge in an industry and the relative strength of the twelve types of knowledge among the companies • A total of 58 responses were collected, among which 17 for semiconductor, 16 for IC Design, 15 for banks, and 10 for steel.

  16. Data measurement • IT capabilities: mean score on the IT capability question from experts • Knowledge intensity: mean score of the other 11 types of knowledge • Knowledge diversity: using the entropy to measure it • Firm performance: Earnings per share in the past five years (means and variance)

  17. Relative Importances

  18. IC design Semiconductor foundry Banking Steel Raw material acquisition 0.8977 0.8608 0.7274 0.7401 Production 0.7180 0.7686 0.7921 0.8023 Distribution 0.7901 0.9489 0.7407 0.9265 Marketing 0.6600 0.8357 0.7096 0.8247 Customer services 0.8008 0.7446 0.1027 0.7342 Strategic planning 0.6704 0.6390 0.8388 0.8106 General mgmt 0.7866 0.8625 0.8415 0.7020 Finance mgmt 0.5080 0.7869 0.8711 0.7803 Quality mgmt 0.7961 0.5822 0.9249 0.8780 Human resources mgmt 0.7750 0.7334 0.9120 0.8691 R&D 0.7709 0.8471 0.8013 0.6753 IT applications 0.9392 0.7617 0.9130 0.8231 All constructs 0.9576 0.9459 0.9508 0.9627 Data Reliability

  19. Results from Path Analysis

  20. Hypotheses Industry H11 H12 H21 H22 H31 H32 IC Design 0.675*** 0.326** 0.630*** 0.565*** ns -0.25* Semiconductor 0.718*** 0.375*** 0.732*** ns -0.375*** -0.195 Banking 0.621*** 0.436** ns ns -0.283* -0.272 Steel 0.724*** 0.364*** 0.502*** -0.351* -0.429** ns Industrial Differences

  21. Effect of Knowledge Breadth • We choose different number of knowledge types and see how knowledge breadth would affect the hypotheses • Stepwise analysis that removed one knowledge category ranked the least important by experts at a time, and repeated the path analysis for 9 times.

  22. Hypotheses Knowledge H11 H12 H21 H22 H31 H32 Top 10 0.740*** 0.414*** 0.619*** 0.502*** -0.204*** -0.127** Top 9 0.719*** 0.387*** 0.617*** 0.505*** -0.195*** -0.137** Top 8 0.743*** 0.422*** 0.603*** 0.499*** -0.192*** -0.155* Top 7 0.700*** 0.365*** 0.602*** 0.514*** -0.183** -0.170** Top 6 0.711*** 0.368*** 0.635*** 0.511*** -0.196*** -0.157* Top 5 0.699*** 0.338*** 0.636*** 0.510*** -0.163** -0.145** Top 4 0.697*** 0.339*** 0.608*** 0.477*** -0.162** -0.142* Top 3 0.705*** 0.322*** 0.535*** 0.433*** ns ns Top 2 0.669*** 0.184** 0.491*** 0.392*** ns -0.154** Models with Different Knowledge Spread

  23. Hypotheses  Knowledge category IC design Semiconductor Banking Steel Top 11 - - - 0.502*** Top 10 0.594*** 0.535*** ns 0.496** Top 9 0.599*** 0.543*** ns 0.510*** Top 8 0.587*** 0.573*** ns 0.503*** Top 7 0.612*** 0.571*** ns 0.505** Top 6 0.612*** 0.600*** ns 0.539*** Top 5 0.620*** 0.601*** ns 0.565*** Top 4 0.550*** 0.604*** ns 0.587*** Top 3 0.541*** 0.659*** ns 0.540*** Top 2 0.412*** 0.627*** ns ns Effects of Knowledge Breadth by Industry H21: knowledge intensity on performance

  24. Major Observations • IT affects the intensity and diversity of organizational knowledge • Higher knowledge intensity improves the average firm performance but reduces the stability (increases variance) • Higher knowledge diversity reduces firm performance, but increases performance stability

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